:jddlmZmZmZmZddlZddlmZddlmZddl m Z ddl m Z ddl mZmZe sdgZGd d e Zy) )AnyOptionalSequenceUnionN)Tensor) _crps_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEz%ContinuousRankedProbabilityScore.plotceZdZUdZdZeed<dZeed<dZeed<dZ e ed<e ed<e ed <d e d d ffd Z de de d d fdZd e fdZ ddeee ee fdeed efdZxZS) ContinuousRankedProbabilityScoreaComputes continuous ranked probability score. .. math:: CRPS(F, y) = \int_{-\infty}^{\infty} (F(x) - 1_{x \geq y})^2 dx where :math:`F` is the predicted cumulative distribution function and :math:`y` is the true target. The metric is usually used to evaluate probabilistic regression models, such as forecasting models. A lower CRPS indicates a better forecast, meaning that forecasted probabilities are closer to the true observed values. CRPS can also be seen as a generalization of the brier score for non binary classification problems. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Predicted float tensor with shape ``(N,d)`` - ``target`` (:class:`~torch.Tensor`): Ground truth float tensor with shape ``(N,d)`` As output of ``forward`` and ``compute`` the metric returns the following output: - ``cosine_similarity`` (:class:`~torch.Tensor`): A float tensor with the cosine similarity Args: reduction: how to reduce over the batch dimension using 'sum', 'mean' or 'none' (taking the individual scores) kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torch import randn >>> from torchmetrics.regression import ContinuousRankedProbabilityScore >>> preds = randn(10, 5) >>> target = randn(10) >>> crps = ContinuousRankedProbabilityScore() >>> crps(preds, target) tensor(0.7731) Fis_differentiablehigher_is_betterfull_state_updategplot_lower_boundscoretotalkwargsreturnNc t|di||jdtjdd|jdtjddy)Nrsum)defaultdist_reduce_fxr)super__init__ add_statetorchzeros)selfr __class__s q/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/regression/crps.pyrz)ContinuousRankedProbabilityScore.__init__HsG "6" w AuM w AuMpredstargetct||\}}}|xjtj||z z c_|xj|z c_y)zUpdate state with predictions and targets. Args: preds: Predictions from model target: Ground truth values N)rrr rr)r"r&r' batch_sizediff ensemble_sums r$updatez'ContinuousRankedProbabilityScore.updateMsA*6eV)D& D, eii| 344  j r%c4|j|jz S)z;Compute the continuous ranked probability score over state.)rr)r"s r$computez(ContinuousRankedProbabilityScore.computeYszzDJJ&&r%valaxc&|j||S)aOPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import ContinuousRankedProbabilityScore >>> metric = ContinuousRankedProbabilityScore() >>> metric.update(randn(10,5), randn(10)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import ContinuousRankedProbabilityScore >>> metric = ContinuousRankedProbabilityScore() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,5), randn(10))) >>> fig, ax = metric.plot(values) )_plot)r"r/r0s r$plotz%ContinuousRankedProbabilityScore.plot]sPzz#r""r%)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrr,r.rrrr r r3 __classcell__)r#s@r$rrs D$t#"d"#t#!e! M MNNN !F !F !t !'' _c(#E&(6*:":;<(#IQRZI[(# (#r%r)typingrrrrr r'torchmetrics.functional.regression.crpsrtorchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r __doctest_skip__rrr%r$rBs921 @&@@?@h#vh#r%